AI Agent Logging vs Monitoring: Most Teams Confuse These

Most teams building AI agent systems believe they have monitoring in place. What they actually have is logging. These are not the same thing — and the difference matters when your agent starts behaving unexpectedly at 2 AM. What Logging Gives You Logs are a record of what happened. Useful for post-mortem. Terrible for real-time intervention. A well-logged AI agent can produce 10,000 lines of output per day. Good luck finding the signal when something goes wrong. Logging answers: What did the agent do? What Monitoring Gives You Monitoring answers: Should I intervene right now? For AI agents, that requires three things: Structured state after each action — machine-readable, not buried in a log file Cost per run — if a \$0.02 task suddenly costs \$0.80, you want to know before it runs 50 more times Escalation flags — a dedicated output (outbox.json) where the agent writes when it needs human input The Three-File Monitoring Stack current-task.json — status, cost_so_far, next_step after eac